Random Forests In A Glassworks: Knowledge Discovery From Industrial Data

This paper presents the use of random forests for knowledge discovery from large data sets. The data was obtained from sensors of glassworks technological process monitoring system. Based on original and standardized data, several random forest models were created. Then they were used to establish a significance ranking of explanatory variables. The ranking presents information about explanatory variables that most affect the product defects.